In Silico Modeling of Drugs Against Coronaviruses - Computational Tools and Protocols (Paperback, 1st ed. 2021)

This essential volume explores a variety of tools and protocols of structure-based (homology modeling, molecular docking, molecular dynamics, protein-protein interaction network) and ligand-based (pharmacophore mapping, quantitative structure-activity relationships or QSARs) drug design for ranking and prioritization of candidate molecules in search of effective treatment strategy against coronaviruses. Beginning with an introductory section that discusses coronavirus interactions with humanity and COVID-19 in particular, the book then continues with sections on tools and methodologies, literature reports and case studies, as well as online tools and databases that can be used for computational anti-coronavirus drug research. Written for the Methods in Pharmacology and Toxicology series, chapters include the kind of practical detail and implementation advice that ensures high quality results in the lab. Comprehensive and timely, In Silico Modeling of Drugs Against Coronaviruses: Computational Tools and Protocols is an ideal reference for researchers working on the development of novel anti-coronavirus drugs for SARS-CoV-2 and for coronaviruses that will likely appear in the future.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Series:	Methods in Pharmacology and Toxicology
Release date:	August 2021
First published:	2021
Editors:	Kunal Roy
Dimensions:	254 x 178mm (L x W)
Format:	Paperback
Pages:	788
Edition:	1st ed. 2021
ISBN-13:	978-1-07-161368-9
Categories:	Books > Computing & IT > Applications of computing > Computer modelling & simulation Books > Medicine > Other branches of medicine > Pathology > Medical microbiology & virology Books > Medicine > Other branches of medicine > Pharmacology > General Books > Science & Mathematics > Chemistry > Physical chemistry > Quantum & theoretical chemistry Books > Professional & Technical > Biochemical engineering > Biotechnology > General
LSN:	1-07-161368-5
Barcode:	9781071613689

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